1. Field of the Invention
The present invention relates to information processing systems and the like for recommending products or services for users. The present invention also relates to information processing apparatuses and the like for acquiring information regarding, for example, the leading degree of users or trend shift of products etc., using behavior history information such as product purchase of users.
2. Description of Related Art
Conventionally, there is an information processing system for simultaneously realizing both a content recommendation with more pertinence based on the name and the value of an item that a user is strongly interested in and a content recommendation in consideration of sequentiality of content utilization (see Patent Document 1). This system has a content usage history information storage and management portion in which content usage history information of a user is stored and managed, a content usage shift information calculating portion that calculates content usage shift information based on the content usage history information, a content usage shift information storage and management portion in which the content usage shift information is stored and managed, a content metadata information storage and management portion in which content metadata information is stored and managed, and a content recommendation information generating portion that generates content recommendation information based on the content usage history information, the content usage shift information, and the content metadata information.
Furthermore, there is a system for extracting characteristics of each item name for an individual, and recommending a content based on the characteristics of each item name for the individual (see Patent Document 2). In this system, user's item-categorized preference information of a targeted user is acquired with respect to preset item names. Reference is made to the acquired user's item-categorized preference information, and if an item name appears for the number of times equal to or larger than a threshold value preset for the item name, then its item value is extracted. Then, content information containing this item value as the value of a target item name is acquired, and the acquired content information is recommended for the user.
Furthermore, there is a system for improving the possibility of realizing product purchase and for providing a comprehensive recommendation service (see Patent Document 3). In this system, if a user accesses a server of a music distribution shop A via a network connection service using a mobile phone and purchases, for example, music software, then the server of the shop A transmits the purchase information to a center, and the center searches for concert information of the singer from its recommendation rules, and transmits the recommendation to the mobile phone via a network connection service. Also, in this system, if the user purchases a concert ticket using the mobile phone from a server of a ticket shop B, then the server of the shop B transmits the purchase information to the center, and the center searches for reservation statuses of an airplane and the like on the concert day in this purchase information, from its recommendation rules, and transmits the recommendation to the mobile phone.
Furthermore, as related techniques, there are algorithms for dividing communities (Non-Patent Documents 1, 2, and 3).
Furthermore, conventionally, in order to judge a trend in the market, there is a market information analyzing apparatus for making it possible for keywords indicating values of consumers to be easily found (see Patent Document 4).    [Patent Document 1] JP 2005-293384A (p. 1, FIG. 1 etc.)    [Patent Document 2] JP 2004-362011A (p. 1, FIG. 1 etc.)    [Patent Document 3] JP 2002-117292A (p. 1, FIG. 1 etc.)    [Patent Document 4] JP 2005-92721A (p. 1, FIG. 1 etc.)    [Non-Patent Document 1] M. E. J. Newman and one other, “Finding and evaluating community structure in networks” Aug. 11, 2003    [Non-Patent Document 2] Aaron Clauset, “Finding local community structure in networks” Feb. 21, 2005    [Non-Patent Document 3] Aaron Clauset and two others, “Finding community structure in very large networks” Aug. 30, 2004)